Rheologica Acta最新文献

When it comes to the measurement of yield stress, the experimental procedure appears to play a significant role. Using a series of experiments, in which the effects of time dependence and shear banding were identified and taken into account, we determined the dynamic and static yield stresses of the materials as unique, test-independent properties. We studied the shear rheological properties of an aqueous suspension of Laponite^®, which is a highly time-dependent (thixotropic) material. To minimize the irreversible effect of aging on its material properties, the Laponite^® dispersion was aged for 347 days under a controlled environment. For comparison, an aqueous solution of Carbopol^®—a slightly time-dependent material—was also investigated. The peak values of the shear stress evolution in constant shear rate tests were compared with the static and dynamic yield stress values. We noticed that, as the shear rate is reduced the peak stress value tends asymptotically to the dynamic yield stress for the slightly time-dependent material, but to slightly above the static yield stress for the thixotropic material. For the Laponite^® suspension, at relatively low shear rates, we observed that peak stresses are influenced by shear banding. By simulating stress evolution curves using stress step-changes, we eliminated the influence of shear banding and discovered that the lowest yielding point coincides with the static yield stress. In addition, we provided the complete flow curve for the Laponite^® suspension, showing the role of the static and dynamic yield stresses, and the unattainable zone which is closely related to steady shear banding effects.

The macroscopic properties of particle-filled polymer melts depend sensitively on the state of particle dispersion and the structure and dynamics of the interfacial polymer layer, which, in turn, are governed by factors like polymer molecular weight (M_w), particle concentration (C), and particle-polymer interfacial interactions. However, the combined effect of these factors on the macroscopic properties is far from fully understood, especially for polymers filled with anisotropic particles. In this work, we investigate the combined effect of M_w, C, and polymer end-group (methyl, Me or hydroxyl, OH) on the dynamic viscoelastic behavior of polydimethylsiloxane (PDMS)/clay composites. The linear viscoelastic behavior of these composites follows a non-monotonic dependence on M_w, which varies considerably with a modification in C or the polymer end-group. Furthermore, for both Me-PDMS/clay and OH-PDMS/clay composites, the non-linear tests reveal either strain softening-hardening-softening or sustained softening beyond the linear regime, depending on the combination of C and M_w. The critical strains for the onset of softening and hardening vary differently with M_w for different combinations of C and the polymer end-group. Our results suggest that the morphology and rheological behavior of these composites are dictated by a complex interplay of various competing effects, namely, particle agglomeration, interfacial polymer packing and density, entanglements, and bridging interactions. These findings give insight into tailoring the properties of polymer composites by adjusting the combination of C, M_w, and particle-polymer interactions.

Graphical abstract

Techniques for evaluating the micromechanical properties of materials are crucial in engineering fields. In previous studies, many researchers have utilized atomic force microscopy (AFM) to address these subjects. However, there are few data on dispersion systems, such as slurries and creams, due to the AFM tip having a nanoscale length. These materials are essential in industrial and engineering settings, requiring an accurate evaluation in a manner similar to AFM. Hence, we focus on ultrahigh accuracy and sensitive spherical nanoindentation (SNI), allowing the measurement of tissue-level features at the surface layer to characterize this soft matter. In this study, we show that SNI potentially measures the local spatial properties of concentrated dispersion fluids, especially oil-in-water (O/W) emulsions with various multilamellar structures. We set the parameter t_e for considering the organization of an equilibrium state consisting of the energy release rate and the work of adhesion on the Johnson–Kendall–Roberts (JKR) predictions. An important consequence of introducing t_e is that the results obtained by SNI match the theoretical JKR values for large t_e, suggesting that we can evaluate the microscopic properties more accurately using the classical JKR model. We find that the local features are affected by the lamellar bilayers and the work of adhesion Δγ grows monotonically with increases in space occupied by lamellar structures. Since viscosity effects, such as mechanical energy dissipation and interpenetration, appear as a part of Δγ, the behavior of Δγ clearly shows the microscopic characteristics of the O/W emulsions.

Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite^® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

An overarching challenge in rheology is to develop constitutive models for complex fluids for which we lack accurate first principles theory. A further challenge is that most experiments probing dynamical structure and rheology do so only in very simple flow fields that are not characteristic of the complex deformation histories experienced by material in a processing application. A recently developed experimental methodology holds potential to overcome this challenge by incorporating a fluidic four-roll mill (FFoRM) into scanning small-angle X-ray scattering instrumentation (sSAXS) (Corona, P. T. et al. Sci. Rep. 8, 15559 (2018); Corona, P. T. et al. Phys. Rev. Mater 6, 045603 (2022)) to rapidly generate large data sets of scattering intensity for complex fluids along diverse Lagrangian flow histories. To exploit this uniquely rich FFoRM-sSAXS data, we propose a machine learning framework, Scattering-Informed Microstructure Prediction under Lagrangian Evolution (SIMPLE), which uses FFoRM-sSAXS data to learn an evolution equation for the scattering intensity and an associated tensorial differential constitutive equation for the stress. The framework incorporates material frame indifference and invariance to arbitrary rotations by data preprocessing. We use autoencoders to find an efficient reduced order model for the scattering intensity and neural network ordinary differential equations to predict the time evolution of the model coordinates. The framework is validated on a synthetic FFoRM-sSAXS data set for a dilute rigid rod suspension. The model accurately predicts microstructural evolution and rheology for flows that differ significantly from those used in training. SIMPLE is compatible with but does not require material-specific constraints or assumptions.

Developing constitutive models that can describe a complex fluid’s response to an applied stimulus has been one of the critical pursuits of rheologists. The complexity of the models typically goes hand-in-hand with that of the observed behaviors and can quickly become prohibitive depending on the choice of materials and/or flow protocols. Therefore, reducing the number of fitting parameters by seeking compact representations of those constitutive models can obviate extra experimentation to confine the parameter space. To this end, fractional derivatives in which the differential response of matter accepts non-integer orders have shown promise. Here, we develop neural networks that are informed by a series of different fractional constitutive models. These fractional rheology-informed neural networks (RhINNs) are then used to recover the relevant parameters (fractional derivative orders) of three fractional viscoelastic constitutive models, i.e., fractional Maxwell, Kelvin-Voigt, and Zener models. We find that for all three studied models, RhINNs recover the observed behavior accurately, although in some cases, the fractional derivative order is recovered with significant deviations from what is known as ground truth. This suggests that extra fractional elements are redundant when the material response is relatively simple. Therefore, choosing a fractional constitutive model for a given material response is contingent upon the response complexity, as fractional elements embody a wide range of transient material behaviors.

Extrusion molding is an important method in the polymer processing industry. The stress concentration of polymer melts can easily occur at the contraction channel, especially at the contraction exit during extrusion molding, which causes volume defects in the final parts. To eliminate or minimize volume defects, this study examined the effects of contraction profiles and contraction lengths on the rheological behaviors of polystyrene melts based on numerical methods and algorithms in the current study. The contraction profiles included abrupt contraction, V-shaped contraction, hyperbolic contraction, and elliptic contraction geometries at different contraction lengths. A single-mode Rolie-Poly model was employed to describe the stress–strain relationship of polystyrene melt. Additionally, the finite volume method and SIMPLE algorithm were used to discretize and solve the governing equations of the fluid in a 4:1 contraction flow. Numerical simulations of the principal stress difference (PSD), stretch ratio, and velocity of polystyrene melt in the aforementioned contraction geometries were implemented. The numerical results indicate that contraction profiles and contraction length are two major factors affecting the rheological behaviors of polystyrene melts in contraction flows based on the same contraction ratio and flow rate. V-shaped contraction, hyperbolic contraction, and elliptic contraction geometries can reduce stress concentration compared to abrupt contraction. Thus, during extrusion molding, it is better to use the elliptic contraction profile with adequate contraction length to eliminate or minimize defects in parts caused by stress concentration at the sharp edge exit.

Graphical abstract

The elongational rheology of blends of a polystyrene (PS) Pom-Pom with two linear polystyrenes was recently reported by Hirschberg et al. (J. Rheol. 2023, 67:403–415). The Pom-Pom PS280k-2x22-22k with a self-entangled backbone (M_w,bb = 280 kg/mol) and 22 entangled sidearms (M_w,a = 22 kg/mol) at each of the two branch points was blended at weight fractions from 75 to 2 wt% with two linear polystyrenes (PS) having M_w of 43 kg/mol (PS43k) and 90 kg/mol (PS90k), respectively. While the pure Pom-Pom shows strong strain hardening in elongational flow (SHF > 100), strain hardening (SHF > 10) is still observed in Pom-Pom/linear blends containing only 2 wt% of Pom-Pom. The elongational start-up viscosities of the blends with Pom-Pom weight fractions above 10 wt% are well described by the Molecular Stress Function (MSF) model, however, requiring two nonlinear fit parameters. Here we show that quantitative and parameter-free modeling of the elongational viscosity data is possible by the Hierarchical Multi-mode Molecular Stress Function (HMMSF) model based on the concepts of hierarchical relaxation and dynamic dilution. In addition, we investigated the elongational viscosity of a blend consisting of 20 wt% Pom-Pom PS280k-2x22-22k and 80 wt% of a PS star with 11 arms of M_w,a = 25 kg/mol having a similar span molecular weight as PS43k and similar M_w,a as the Pom-Pom. This work might open up possibilities toward polymer upcycling of less-defined polymers by adding a polymer with optimized topology to gain the intended strain hardening, e.g., for film blowing applications.

Graphical Abstract

Conductive polypyrrole nanotubes were synthesized with a two-step one-pot synthesis. During synthesis, the nanotubes were decorated with magnetite nanoparticles at different concentrations granting them magnetic properties. The characterization of the tubes revealed differences from the theoretical reactions. A bidisperse magnetorheological fluid (MRF) was prepared by mixing the composite polypyrrole nanotubes/magnetite nanoparticles with commercial carbonyl iron spherical microparticles in silicone oil. The rheological properties of the bidisperse system were studied under the presence of magnetic field at room and elevated temperature. An enhancement of the MR effect with the presence of the nanotubes was observed when compared with a standard MRF consisted only of magnetic microparticles. Due to the faster magnetic saturation of the nanotubes, this enhancement is exceptionally high at low magnetic fields. The stability of the system is studied under dynamic conditions where it is revealed that the nanotubes keep the standard particles well dispersed with the sedimentation improving by more than 50%.

This study introduces the Constitutive Neural Network (ConNN) model, a machine learning algorithm that accurately predicts the temporal response of complex fluids under specific deformations. The ConNN model utilizes a recurrent neural network architecture to capture the time dependent stress responses, and the recurrent units are specifically designed to reflect the characteristics of complex fluids (fading memory, finite elastic deformation, and relaxation spectrum), without presuming any equation of motion of the fluid. We demonstrate that the ConNN model can effectively replicate the temporal data generated by the Giesekus model and the Thixotropic-Elasto-Visco-Plastic (TEVP) fluid model under varying shear rates. To test the performance of the trained model, we subject it to an oscillatory shear flow, with periodic reversals in flow direction, which has not been trained on. The ConNN model successfully replicates the shear moduli of the original models, and the trained values of the recurrent parameters match the physical prediction of the original models. However, we do observe a slight deviation in the normal stresses, indicating that further improvements are necessary to achieve more rigorous physical symmetry and improve the model prediction.